Abstract: Decision-making rights are different according to the individuals’ fitness values by the analysis of SI decision-making mechanism. The improvements of QPSO algorithm are proposed to improve searching efficiency and optimal performance by introducing linear weighted operator into algorithm. Sliding mode controller design method of robots trajectory tracking is analyzed. A new sliding mode control law is designed by combining index reaching law and power reaching law. The parameters in sliding mode tracking controller are optimized through PSO algorithm, QPSO algorithm and the improved QPSO algorithm. The design result of optimal tracking controller is validated by two design examples. It shows that tracking controller can control robot to track planned trajectory. Simulation results show that the improved QPSO algorithm can get better effect in optimizing parameters of controllers.

Key words: weighted operator, Quantum Particle Swarm Optimization（QPSO） algorithm, sliding mode, control law, trajectory tracking

摘要： 分析了智能群体的决策机制，发现在智能群体决策过程中，个体粒子参与决策的权利根据个体的优劣程度是不同的，提出了在量子粒子群优化（QPSO）算法中引入线性权重算子进一步提高QPSO算法的搜索效率及优化性能。分析了移动机器人轨迹跟踪控制的滑模变结构控制器设计方法，并采用指数趋近律和幂次趋近律相结合的方法，设计了新的滑模跟踪控制律，使用PSO算法、QPSO算法和改进算法优化了滑模跟踪控制器中的参数，通过两个实例验证了优化后的跟踪控制器的设计效果；设计效果的分析和比较表明了设计的跟踪控制器能够控制机器人实现对既定轨迹的跟踪，仿真结果显示改进QPSO算法能够在轨迹跟踪控制器的参数优化中取得更好的优化效果。

关键词: 权重算子, 量子粒子群优化算法, 滑模, 控制律, 轨迹跟踪